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Process mining in Collaborative Networks: Discovering Real Processes and Interactions
prof.dr.ir. Wil van der Aalstwww.processmining.org
PAGE 1
If I had an hour to save the world, I would spend 59
minutes defining the problem and one minute finding
solutions (Einstein)
PAGE 2
virtual extended enterprises(dynamic) virtual organizations
(professional) virtual communitiesservice oriented computing
internet of things/future internet
process mining
Process Mining: Why?
• physicists study matter and its motion through spacetime,
• chemists study the composition, structure, and properties of matter,
• biologists study evolution, organisms, cells, and genes,
• management science?• computer science?• social science?
PAGE 3
PAGE 5
Process Mining
• Process discovery: "What is really happening?"
• Conformance checking: "Do we do what was agreed upon?"
• Performance analysis: "Where are the bottlenecks?"
• Process prediction: "Will this case be late?"
• Process improvement: "How to redesign this process?"
• Etc.
PAGE 6
• Process discovery: "What is the real curriculum?"• Conformance checking: "Do students meet the prerequisites?"• Performance analysis: "Where are the bottlenecks?"• Process prediction: "Will a student complete his studies (in time)?"• Process improvement: "How to redesign the curriculum?"
PAGE 7
Process mining: Linking events to models
software system
(process)model
eventlogs
modelsanalyzes
discovery
records events, e.g., messages,
transactions, etc.
specifies configures implements
analyzes
supports/controls
extension
conformance
“world”
people machines
organizationscomponents
business processes
PAGE 8
Process mining as a mirror ...
PAGE 9
Where did we apply process mining?
• Municipalities (e.g., Alkmaar, Heusden, etc.)• Government agencies (e.g., Rijkswaterstaat, Centraal
Justitieel Incasso Bureau, Justice department)• Insurance related agencies (e.g., UWV)• Banks (e.g., ING Bank)• Hospitals (e.g., AMC hospital, Catharina hospital)• Multinationals (e.g., DSM, Deloitte)• High-tech system manufacturers and their customers
(e.g., Philips Healthcare, ASML, Thales)• Media companies (e.g. Winkwaves)• ...
Wet Maatschappelijke Ondersteuning (WMO)Harderwijk
• WMO: supporting citizens of municipalities (illness, handicaps, elderly, etc.).
• Examples:• wheelchair, scootmobiel, ...• adaptation of house (elevator), ...• household help, ...
• Event log: • CSV format (Pallas Athena/Futura Technology)• 876 process instances• 5497 events• January 2007- August 2008
Original log
All elements
Model element Event type Occurrences (absolute) Occurrences (relative)Toetsen en beslissen complete 910 16.554%
Rapportage & beschikking complete 872 15.863%
Verzending\dossiervorming complete 844 15.354%
Aanvraag registratie complete 842 15.317%
Slotfase complete 828 15.063%
Administratieve verwerking complete 616 11.206%
Wachten terugmelding zorgaanb. complete 515 9.369%
Retour complete 60 1.092%
Eigen onderzoek complete 6 0.109%
Onderzoek CIZ complete 4 0.073%
Discovered process
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PAGE 17
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Discovered WMO process
144 cases (i.e., requests for adaptation of house)1326 recorded eventsWMO = Wet Maatschappelijke Ondersteuning
PAGE 20
Conformance check of discovered model
activity is sometimes not
performed
good fit 97.9%
drill dow
n
performed while not allowed
both
PAGE 21
Performance analysis
bottleneck
flow time
time from
A to B
PAGE 22
Events sorted by duration
PAGE 23
"Real" animation
PAGE 24
And of course ...
Process Discovery
α
PAGE 26
>,→,||,# relations
• Direct succession: x>y iff for some case x is directly followed by y.
• Causality: x→y iff x>y and not y>x.
• Parallel: x||y iff x>y and y>x
• Choice: x#y iff not x>y and not y>x.
case 1 : task A case 2 : task A case 3 : task A case 3 : task B case 1 : task B case 1 : task C case 2 : task C case 4 : task A case 2 : task B case 2 : task D case 5 : task E case 4 : task C case 1 : task D case 3 : task C case 3 : task D case 4 : task B case 5 : task F case 4 : task D
A>BA>CB>CB>DC>BC>DE>F
A→BA→CB→DC→DE→F
B||CC||B
ABCDACBDEF
PAGE 27
Basic Idea Used by α Algorithm (1)
a b
(a) sequence pattern: a→b
PAGE 28
Basic Idea Used by α Algorithm (2)
a
b
c
(b) XOR-split pattern:a→b, a→c, and b#c
a
b
c
(c) XOR-join pattern:a→c, b→c, and a#b
a
b
c
(b) XOR-split pattern:a→b, a→c, and b#c
PAGE 29
Basic Idea Used by α Algorithm (3)
a
b
c
(d) AND-split pattern:a→b, a→c, and b||c
a
b
c
(e) AND-join pattern:a→c, b→c, and a||b
a
b
c
(d) AND-split pattern:a→b, a→c, and b||c
Example Revisited
PAGE 30
A
B
C
DE
p2
end
p4
p3p1
start
A>BA>CB>CB>DC>BC>DE>F
A→BA→CB→DC→DE→F
B||CC||B
B#EC#E…
Result produced by α algorithm
PAGE 31
Overview of process discovery techniques
• Classical techniques (e.g., learning state machines and the theory of regions): cannot handle concurrency and/or do not generalize (i.e., if it did not happen, it cannot happen).
• Algorithmic techniques• Alpha miner• Alpha+, Alpha++, Alpha#• Heuristic miner• Multi phase miner• ...
• Genetic process mining• Region-based process mining
• State-based regions• Language based regions
PAGE 32
Genetic Mining(Ana Karla Alves de Medeiros et al.)
1. initial population
2. fitness test
3. select best parents4. crossover
5. children
6. mutation
7. new population
A
B
C
E H
F I
J
D
L
K
M
A
B
C
E H
F I
J
L
M
A
B
C
E H
F I
J
L
K
MA
C
H
F I
J
L
K
M
Challenges
• Dealing with semi-structured processes
• Balancing between overfitting and underfitting
• 365!/(365365) ≈ 1.454955E-157 ≈ 0 (1E79 is the estimated number of atoms in the universe)
• Suitable notations/visualizations (executable?, declarative?, ”zoomable”, “abstractable”, etc.)
• Data explosion (mining TB logs)
PAGE 33
PAGE 34
Process Mining Software
ARIS Process Performance Manager
Interstage Automated Business Process Discovery & Visualization
Process Discovery Focus
Futura Reflect
Enterprise Visualization Suite
Comprehend
BPM|one
PAGE 35
Screenshot of ProM 5.0
PAGE 36
Overview
control-flow data organization/resources
time
discovery
conformance
extension
model-based analysis
conversion
alpha miner
genetic miner
social network
miner
dotted chart
conf. checker
Petri nets to EPCs
woflan analysis
LTL checker
CPN generation
> 250 plug-ins in ProM 5.2 !
PAGE 37
Reality ≠ PowerPoint (or Visio)
PAGE 38
Process spectrum
structured(Lasagna)
unstructured(Spaghetti)
PAGE 39
375 houses18640 events
82 different activities
PAGE 40
2712 patients29258 events
264 different activities
PAGE 41
874 patients10478 events
181 different activities
PAGE 42
24 machines154966 events
360 different activities
PAGE 43
37.5% OK62.5% NOK
design reality
PAGE 44
6σ ?1σ !
Process Mining: TomTom for Business Processes
PAGE 46
How can process mining help?
• Good maps?• Navigation by
PowerPoints?• Traffic information?• Where is the next fuel
station?• Who is in charge?• Seamless zoom?• Customizable views?• When will the
destination be reached?
PAGE 47
city highway
PAGE 48
ProM's "real animation"
PAGE 49
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Approach
annotated transition system
eventlog
operational business
processes
process discovery
predictionengine
partial case
prediction
information system
{}A
{A} {A,C}C
{A,B}
B
{A,B,C}C
{A,B,C,D}D
{A,E} {A,D,E}D
E
B
[18,26,44,13,14,40,24]
[34,31] [0,0]
[0,0,0,0,0][6,10,6,6,8]
[22,19]
[12,9,10]
[18,26,44,13,14,40,24]
When? 12-6-2009!
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Input: partial trace and historic information
A B C D C D C D E
past
current state
unknownfuture
partial trace
history
predict
F A G H H H I
processdiscovery
unknown completion
time
predicted completion
time
annotated transition system
eventlog
(A B C D C D C D E)?
(12-6-2009)!
(14-6-2009)!
{}A
{A} {A,C}C
{A,B}
B
{A,B,C}C
{A,B,C,D}D
{A,E} {A,D,E}D
E
B
[18,26,44,13,14,40,24]
[34,31] [0,0]
[0,0,0,0,0][6,10,6,6,8]
[22,19]
[12,9,10]
[18,26,44,13,14,40,24]
PAGE 52
Example: WOZ process in Dutch Municipality
1882 objections triggering 11985
activities
PAGE 53
All 11985 events at a glance
Average flow time is 107 days(with a huge variation)
PAGE 54
For partial traces
corresponding to this state the estimated time
until completion is 8.5 days
PAGE 55
Cross validation: training set and test setMean
Average Error (MAE)
rooted MSE
MAPE
PAGE 56
Some results
Process mining in Collaborative Networks: An Outlook
PAGE 58
Collaborative Networksand Process Mining?
PAGE 59
(Taken from “L. Camarinha-Matos and H. Afsarmanesh, Collaborative networks: a new scientific Discipline, Journal of Intelligent Manufacturing, 16, 439–452, 2005”)
Technological Perspective:Process mining and SOA
PAGE 60
BPEL choreography
Community Web Sites
PAGE 61
Another Example: Wikipedia
PAGE 62
Conclusion
PAGE 65
Conclusion
• The abundance of event data enables a wide variety of process mining techniques ranging from process discovery to conformance checking.
• A new tool for analyzing collaborative networks.
• Check out ProM with its 250+ plug-ins.• Contribute: case studies, plug-ins, etc.
PAGE 66
Thanks! cf. www.processmining.org
• Wil van der Aalst• Peter van den Brand• Boudewijn van Dongen• Christian Günther• Eric Verbeek• Ana Karla Alves de Medeiros• Anne Rozinat• Minseok Song• Ton Weijters• Remco Dijkman• Gianluigi Greco• Antonella Guzzo• Kristian Bisgaard Lassen• Ronny Mans• Jan Mendling• Vladimir Rubin• Nikola Trcka• Irene Vanderfeesten• Barbara Weber• Lijie Wen
• Mercy Amiyo• Carmen Bratosin• Toon Calders• Jorge Cardoso• Ronald Crooy• Florian Gottschalk• Monique Jansen-Vullers• Peter Khisa Wakholi• Nicolas Knaak• Sven Lambrechts• Joyce Nakatumba• Mariska Netjes• Mykola Pechenizkiy• Maja Pesic• Hajo Reijers• Stefanie Rinderle• Domenico Saccà• Helen Schonenberg• Marc Voorhoeve• Jianmin Wang
• Jan Martijn van der Werf• Martin van Wingerden• Jianhong Ye• Huub de Beer• Elena Casares• Alina Chipaila• Walid Gaaloul• Martijn van Giessel• Shaifali Gupta• Thomas Hoffmann• Peter Hornix• René Kerstjens• Ralf Kramer• Wouter Kunst• Laura Maruster• Andriy Nikolov• Adarsh Ramesh• Jo Theunissen• Kenny van Uden• ...
PAGE 67
Relevant WWW sites
• http://www.processmining.org• http:// promimport.sourceforge.net
• http://prom.sourceforge.net
• http://www.workflowpatterns.com
• http://www.workflowcourse.com
• http://www.vdaalst.com
http://www.senternovem.nl/innovatievouchersMKB 2.500 – 7.500 euro
“Not everything that counts can be counted, and not everything that can be counted counts” (Einstein)